We’re looking at building something more ambitious than single-task automations. The idea is to have multiple AI agents coordinate on an end-to-end process: one agent extracts and validates data, another analyzes it for patterns, a third generates a report, and a fourth handles communication back to our team members.
On the surface, this sounds like it would be massively more efficient. We’re essentially replacing a multi-person workflow with coordinated AI agents. But I’m struggling to understand where the actual cost and complexity scaling starts to hurt the ROI picture.
Like, if we’re running four separate model instances and each one costs money, plus there’s orchestration overhead and error handling between them, does the total cost spiral in a way that eats the savings? And when something fails—which it will—how much manual intervention does it take to get things back on track? Does that complexity become its own cost?
The other thing I’m unsure about: with multiple agents working together, how do you actually validate that they’re doing what you expect? A single automation workflow is straightforward to audit. But when you have agents coordinating, making decisions based on each other’s outputs, is it harder to trust the output?
I’m trying to figure out if the ROI scales linearly as you add more agents, or if there’s a complexity ceiling where adding the fifth agent costs more than the time savings it provides.